Effects of forward model errors on EEG source localization.

Subject-specific four-layer boundary element method (BEM) electrical forward head models for four participants, generated from magnetic resonance (MR) head images using NFT ( www.sccn.ucsd.edu/wiki/NFT ), were used to simulate electroencephalographic (EEG) scalp potentials at 256 recorded electrode positions produced by single current dipoles of a 3-D grid in brain space. Locations of these dipoles were then estimated using gradient descent within five template head models fit to the electrode positions. These were: a spherical model, three-layer and four-layer BEM head models based on the Montreal Neurological Institute (MNI) template head image, and these BEM models warped to the recorded electrode positions. Smallest localization errors (4.1-6.2 mm, medians) were obtained using the electrode-position warped four-layer BEM models, with largest localization errors (~20 mm) for most basal brain locations. When we increased the brain-to-skull conductivity ratio assumed in the template model scalp projections from the simulated value (25:1) to a higher value (80:1) used in earlier studies, the estimated dipole locations moved outwards (12.4 mm, median). We also investigated the effects of errors in co-registering the electrode positions, of reducing electrode counts, and of adding a fifth, isotropic white matter layer to one individual head model. Results show that when individual subject MR head images are not available to construct subject-specific head models, accurate EEG source localization should employ a four- or five-layer BEM template head model incorporating an accurate skull conductivity estimate and warped to 64 or more accurately 3-D measured and co-registered electrode positions

Electrocortical source imaging of intracranial EEG data in epilepsy

Abstract — Here we report first results of numerical methods for modeling the dynamic structure and evolution of epileptic seizure activity in an intracranial subdural electrode (iEEG, ECoG) recording from a patient with partial refractory epilepsy. A 15-min dataset containing two seizures was decomposed using up to five competing adaptive mixture ICA (AMICA) models. Multiple models modeled early or late ictal, or pre-or post-ictal periods in the data, respectively. To localize sources, a realistic Boundary Element Method (BEM) head model was constructed for the patient with custom open skull and plastic (non-conductive) electrode holder features. Source localization was performed using Sparse Bayesian Learning (SBL) on a dictionary of overlapping multi-scale cortical patches constructed from 80,130 dipoles in gray matter perpendicular to the cortical surface. Remaining mutual information among seizure-model AMICA components was dominated by two dependent component subspaces with largely contiguous source domains localized to superior frontal gyrus and precentral gyrus; these accounted for most of the ictal activity. Similar though much weaker dependent subspaces were also revealed in pre-ictal data by the associated AMICA model. Electrocortical source imaging appears promising both for clinical epilepsy research and for basic cognitive neuroscience research using volunteer patients who must undergo invasive monitoring for medical purposes. I

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

Contemporary active-EEG and EEG-imaging methods show particular promise for studying the development of action planning and social-action representation in infancy and early childhood. Action-related mu suppression was measured in eleven 3-year-old children and their mothers during a ‘live,’ largely unscripted social interac- tion. High-density EEG was recorded from children and synchronized with motion-captured records of children's and mothers' hand actions, and with video recordings. Independent Component Analysis (ICA) was used to sep- arate brain and non-brain source signals in toddlers' EEG records. EEG source dynamics were compared across three kinds of epochs: toddlers' own actions (execution), mothers' actions (observation), and between-turn in- tervals (no action). Mu (6–9 Hz) power was suppressed in left and right somatomotor cortex during both action execution and observation, as reflected by independent components of individual children's EEG data. These mu rhythm components were accompanied by beta-harmonic (~16 Hz) suppression, similar to findings from adults. The toddlers' power spectrum and scalp density projections provide converging evidence of adult-like mu- suppression features. Mu-suppression components' source locations were modeled using an age-specific 4- layer forward head model. Putative sources clustered around somatosensory cortex, near the hand/arm region. The results demonstrate that action-locked, event-related EEG dynamics can be measured, and source- resolved, from toddlers during social interactions with relatively unrestricted social behaviors

EEG imaging of toddlers during dyadic turn-taking: Mu-rhythm modulation while producing or observing social actions

Contemporary active-EEG and EEG-imaging methods show particular promise for studying the development of action planning and social-action representation in infancy and early childhood. Action-related mu suppression was measured in eleven 3-year-old children and their mothers during a ‘live,’ largely unscripted social interac- tion. High-density EEG was recorded from children and synchronized with motion-captured records of children's and mothers' hand actions, and with video recordings. Independent Component Analysis (ICA) was used to sep- arate brain and non-brain source signals in toddlers' EEG records. EEG source dynamics were compared across three kinds of epochs: toddlers' own actions (execution), mothers' actions (observation), and between-turn in- tervals (no action). Mu (6–9 Hz) power was suppressed in left and right somatomotor cortex during both action execution and observation, as reflected by independent components of individual children's EEG data. These mu rhythm components were accompanied by beta-harmonic (~16 Hz) suppression, similar to findings from adults. The toddlers' power spectrum and scalp density projections provide converging evidence of adult-like mu- suppression features. Mu-suppression components' source locations were modeled using an age-specific 4- layer forward head model. Putative sources clustered around somatosensory cortex, near the hand/arm region. The results demonstrate that action-locked, event-related EEG dynamics can be measured, and source- resolved, from toddlers during social interactions with relatively unrestricted social behaviors

EEGLAB, SIFT, NFT, BCILAB, and ERICA: New Tools for Advanced EEG Processing

We describe a set of complementary EEG data collection and processing tools recently developed at the Swartz Center for Computational Neuroscience (SCCN) that connect to and extend the EEGLAB software environment, a freely available and readily extensible processing environment running under Matlab. The new tools include (1) a new and flexible EEGLAB STUDY design facility for framing and performing statistical analyses on data from multiple subjects; (2) a neuroelectromagnetic forward head modeling toolbox (NFT) for building realistic electrical head models from available data; (3) a source information flow toolbox (SIFT) for modeling ongoing or event-related effective connectivity between cortical areas; (4) a BCILAB toolbox for building online brain-computer interface (BCI) models from available data, and (5) an experimental real-time interactive control and analysis (ERICA) environment for real-time production and coordination of interactive, multimodal experiments